We’re in the midst of a global mental-­health crisis. More than a billion people worldwide suffer from a mental-health condition, according to the World Health Organization. The prevalence of anxiety and depression is growing in many demographics, particularly young people, and suicide is claiming hundreds of thousands of lives globally each year.

Given the clear demand for accessible and affordable mental-health services, it’s no wonder that people have looked to artificial intelligence for possible relief. Millions are already actively seeking therapy from popular chatbots like OpenAI’s ChatGPT and Anthropic’s Claude, or from specialized psychology apps like Wysa and Woebot. On a broader scale, researchers are exploring AI’s potential to monitor and collect behavioral and biometric observations using wearables and smart devices, analyze vast volumes of clinical data for new insights, and assist human mental-health professionals to help prevent burnout. 

But so far this largely uncontrolled experiment has produced mixed results. Many people have found solace in chatbots based on large language models (LLMs), and some experts see promise in them as therapists, but other users have been sent into delusional spirals by AI’s hallucinatory whims and breathless sycophancy. Most tragically, multiple families have alleged that chatbots contributed to the suicides of their loved ones, sparking lawsuits against companies responsible for these tools. In October, OpenAI CEO Sam Altman revealed in a blog post that 0.15% of ChatGPT users “have conversations that include explicit indicators of potential suicidal planning or intent.” That’s roughly a million people sharing suicidal ideations with just one of these software systems every week.

The real-world consequences of AI therapy came to a head in unexpected ways in 2025 as we waded through a critical mass of stories about human-chatbot relationships, the flimsiness of guardrails on many LLMs, and the risks of sharing profoundly personal information with products made by corporations that have economic incentives to harvest and monetize such sensitive data. 

Several authors anticipated this inflection point. Their timely books are a reminder that while the present feels like a blur of breakthroughs, scandals, and confusion, this disorienting time is rooted in deeper histories of care, technology, and trust. 

LLMs have often been described as “black boxes” because nobody knows exactly how they produce their results. The inner workings that guide their outputs are opaque because their algorithms are so complex and their training data is so vast. In mental-health circles, people often describe the human brain as a “black box,” for analogous reasons. Psychology, psychiatry, and related fields must grapple with the impossibility of seeing clearly inside someone else’s head, let alone pinpointing the exact causes of their distress. 

These two types of black boxes are now interacting with each other, creating unpredictable feedback loops that may further impede clarity about the origins of people’s mental-­health struggles and the solutions that may be possible. Anxiety about these developments has much to do with the explosive recent advances in AI, but it also revives decades-old warnings from pioneers such as the MIT computer scientist Joseph Weizenbaum, who argued against computerized therapy as early as the 1960s.  

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Charlotte Blease, a philosopher of medicine, makes the optimist’s case in Dr. Bot: Why Doctors Can Fail Us—and How AI Could Save Lives. Her book broadly explores the possible positive impacts of AI in a range of medical fields. While she remains clear-eyed about the risks, warning that readers who are expecting “a gushing love letter to technology” will be disappointed, she suggests that these models can help relieve patient suffering and medical burnout alike.

“Health systems are crumbling under patient pressure,” Blease writes. “Greater burdens on fewer doctors create the perfect petri dish for errors,” and “with palpable shortages of doctors and increasing waiting times for patients, many of us are profoundly frustrated.”

Blease believes that AI can not only ease medical professionals’ massive workloads but also relieve the tensions that have always existed between some patients and their caregivers. For example, people often don’t seek needed care because they are intimidated or fear judgment from medical professionals; this is especially true if they have mental-health challenges. AI could allow more people to share their concerns, she argues. 

But she’s aware that these putative upsides need to be weighed against major drawbacks. For instance, AI therapists can provide inconsistent and even dangerous responses to human users, according to a 2025 study, and they also raise privacy concerns, given that AI companies are currently not bound by the same confidentiality and HIPAA standards as licensed therapists. 

While Blease is an expert in this field, her motivation for writing the book is also personal: She has two siblings with an incurable form of muscular dystrophy, one of whom waited decades for a diagnosis. During the writing of her book, she also lost her partner to cancer and her father to dementia within a devastating six-month period. “I witnessed first-hand the sheer brilliance of doctors and the kindness of health professionals,” she writes. “But I also observed how things can go wrong with care.”

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A similar tension animates Daniel Oberhaus’s engrossing book The Silicon Shrink: How Artificial Intelligence Made the World an Asylum. Oberhaus starts from a point of tragedy: the loss of his younger sister to suicide. As Oberhaus carried out the “distinctly twenty-first-century mourning process” of sifting through her digital remains, he wondered if technology could have eased the burden of the psychiatric problems that had plagued her since childhood.

“It seemed possible that all of this personal data might have held important clues that her mental health providers could have used to provide more effective treatment,” he writes. “What if algorithms running on my sister’s smartphone or laptop had used that data to understand when she was in distress? Could it have led to a timely intervention that saved her life? Would she have wanted that even if it did?”

This concept of digital phenotyping—in which a person’s digital behavior could be mined for clues about distress or illness—seems elegant in theory. But it may also become problematic if integrated into the field of psychiatric artificial intelligence (PAI), which extends well beyond chatbot therapy.

Oberhaus emphasizes that digital clues could actually exacerbate the existing challenges of modern psychiatry, a discipline that remains fundamentally uncertain about the underlying causes of mental illnesses and disorders. The advent of PAI, he says, is “the logical equivalent of grafting physics onto astrology.” In other words, the data generated by digital phenotyping is as precise as physical measurements of planetary positions, but it is then integrated into a broader framework—in this case, psychiatry—that, like astrology, is based on unreliable assumptions.  

Oberhaus, who uses the phrase “swipe psychiatry” to describe the outsourcing of clinical decisions based on behavioral data to LLMs, thinks that this approach cannot escape the fundamental issues facing psychiatry. In fact, it could worsen the problem by causing the skills and judgment of human therapists to atrophy as they grow more dependent on AI systems. 

He also uses the asylums of the past—in which institutionalized patients lost their right to freedom, privacy, dignity, and agency over their lives—as a touchstone for a more insidious digital captivity that may spring from PAI. LLM users are already sacrificing privacy by telling chatbots sensitive personal information that companies then mine and monetize, contributing to a new surveillance economy. Freedom and dignity are at stake when complex inner lives are transformed into data streams tailored for AI analysis. 

AI therapists could flatten humanity into patterns of prediction, and so sacrifice the intimate, individualized care that is expected of traditional human therapists. “The logic of PAI leads to a future where we may all find ourselves patients in an algorithmic asylum administered by digital wardens,” Oberhaus writes. “In the algorithmic asylum there is no need for bars on the window or white padded rooms because there is no possibility of escape. The asylum is already everywhere—in your homes and offices, schools and hospitals, courtrooms and barracks. Wherever there’s an internet connection, the asylum is waiting.”

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Eoin Fullam, a researcher who studies the intersection of technology and mental health, echoes some of the same concerns in Chatbot Therapy: A Critical Analysis of AI Mental Health Treatment. A heady academic primer, the book analyzes the assumptions underlying the automated treatments offered by AI chatbots and the way capitalist incentives could corrupt these kinds of tools.  

Fullam observes that the capitalist mentality behind new technologies “often leads to questionable, illegitimate, and illegal business practices in which the customers’ interests are secondary to strategies of market dominance.”

That doesn’t mean that therapy-bot makers “will inevitably conduct nefarious activities contrary to the users’ interests in the pursuit of market dominance,” Fullam writes. 

But he notes that the success of AI therapy depends on the inseparable impulses to make money and to heal people. In this logic, exploitation and therapy feed each other: Every digital therapy session generates data, and that data fuels the system that profits as unpaid users seek care. The more effective the therapy seems, the more the cycle entrenches itself, making it harder to distinguish between care and commodification. “The more the users benefit from the app in terms of its therapeutic or any other mental health intervention,” he writes, “the more they undergo exploitation.” 

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This sense of an economic and psychological ouroboros—the snake that eats its own tail—serves as a central metaphor in Sike, the debut novel from Fred Lunzer, an author with a research background in AI. 

Described as a “story of boy meets girl meets AI psychotherapist,” Sike follows Adrian, a young Londoner who makes a living ghostwriting rap lyrics, in his romance with Maquie, a business professional with a knack for spotting lucrative technologies in the beta phase. 

The title refers to a splashy commercial AI therapist called Sike, uploaded into smart glasses, that Adrian uses to interrogate his myriad anxieties. “When I signed up to Sike, we set up my dashboard, a wide black panel like an airplane’s cockpit that showed my daily ‘vitals,’” Adrian narrates. “Sike can analyze the way you walk, the way you make eye contact, the stuff you talk about, the stuff you wear, how often you piss, shit, laugh, cry, kiss, lie, whine, and cough.”

In other words, Sike is the ultimate digital phenotyper, constantly and exhaustively analyzing everything in a user’s daily experiences. In a twist, Lunzer chooses to make Sike a luxury product, available only to subscribers who can foot the price tag of £2,000 per month. 

Flush with cash from his contributions to a hit song, Adrian comes to rely on Sike as a trusted mediator between his inner and outer worlds. The novel explores the impacts of the app on the wellness of the well-off, following rich people who voluntarily commit themselves to a boutique version of the digital asylum described by Oberhaus.

The only real sense of danger in Sike involves a Japanese torture egg (don’t ask). The novel strangely sidesteps the broader dystopian ripples of its subject matter in favor of drunken conversations at fancy restaurants and elite dinner parties. 

The sudden ascent of the AI therapist seems startlingly futuristic, as if it should be unfolding in some later time when the streets scrub themselves and we travel the world through pneumatic tubes.

Sike’s creator is simply “a great guy” in Adrian’s estimation, despite his techno-messianic vision of training the app to soothe the ills of entire nations. It always seems as if a shoe is meant to drop, but in the end, it never does, leaving the reader with a sense of non-resolution.

While Sike is set in the present day, something about the sudden ascent of the AI therapist—­in real life as well as in fiction—seems startlingly futuristic, as if it should be unfolding in some later time when the streets scrub themselves and we travel the world through pneumatic tubes. But this convergence of mental health and artificial intelligence has been in the making for more than half a century. The beloved astronomer Carl Sagan, for example, once imagined a “network of computer psychotherapeutic terminals, something like arrays of large telephone booths” that could address the growing demand for mental-health services.

Oberhaus notes that one of the first incarnations of a trainable neural network, known as the Perceptron, was devised not by a mathematician but by a psychologist named Frank Rosenblatt, at the Cornell Aeronautical Laboratory in 1958. The potential utility of AI in mental health was widely recognized by the 1960s, inspiring early computerized psychotherapists such as the DOCTOR script that ran on the ELIZA chatbot developed by Joseph Weizenbaum, who shows up in all three of the nonfiction books in this article.

Weizenbaum, who died in 2008, was profoundly concerned about the possibility of computerized therapy. “Computers can make psychiatric judgments,” he wrote in his 1976 book Computer Power and Human Reason. “They can flip coins in much more sophisticated ways than can the most patient human being. The point is that they ought not to be given such tasks. They may even be able to arrive at ‘correct’ decisions in some cases—but always and necessarily on bases no human being should be willing to accept.”

It’s a caution worth keeping in mind. As AI therapists arrive at scale, we’re seeing them play out a familiar dynamic: Tools designed with superficially good intentions are enmeshed with systems that can exploit, surveil, and reshape human behavior. In a frenzied attempt to unlock new opportunities for patients in dire need of mental-health support, we may be locking other doors behind them.

Becky Ferreira is a science reporter based in upstate New York and author of First Contact: The Story of Our Obsession with Aliens.

npj Digital Medicine, Published online: 30 December 2025; doi:10.1038/s41746-025-02284-9

PIC-SURE: an open-source platform for integrating clinical and genomic data

arXiv:2512.22199v1 Announce Type: new Abstract: Retrieval-Augmented Generation RAG systems enhance large language models by grounding responses in external knowledge bases, but conventional RAG architectures operate with static corpora that cannot evolve from user interactions. We introduce Bidirectional RAG, a novel RAG architecture that enables safe corpus expansion through validated write back of high quality generated responses. Our system employs a multi stage acceptance layer combining grounding verification (NLI based entailment, attribution checking, and novelty detection to prevent hallucination pollution while enabling knowledge accumulation. Across four datasets Natural Questions, TriviaQA, HotpotQA, Stack Overflow with three random seeds 12 experiments per system, Bidirectional RAG achieves 40.58% average coverage nearly doubling Standard RAG 20.33% while adding 72% fewer documents than naive write back 140 vs 500. Our work demonstrates that self improving RAG is feasible and safe when governed by rigorous validation, offering a practical path toward RAG systems that learn from deployment.

arXiv:2512.22334v1 Announce Type: new Abstract: We introduce SciEvalKit, a unified benchmarking toolkit designed to evaluate AI models for science across a broad range of scientific disciplines and task capabilities. Unlike general-purpose evaluation platforms, SciEvalKit focuses on the core competencies of scientific intelligence, including Scientific Multimodal Perception, Scientific Multimodal Reasoning, Scientific Multimodal Understanding, Scientific Symbolic Reasoning, Scientific Code Generation, Science Hypothesis Generation and Scientific Knowledge Understanding. It supports six major scientific domains, spanning from physics and chemistry to astronomy and materials science. SciEvalKit builds a foundation of expert-grade scientific benchmarks, curated from real-world, domain-specific datasets, ensuring that tasks reflect authentic scientific challenges. The toolkit features a flexible, extensible evaluation pipeline that enables batch evaluation across models and datasets, supports custom model and dataset integration, and provides transparent, reproducible, and comparable results. By bridging capability-based evaluation and disciplinary diversity, SciEvalKit offers a standardized yet customizable infrastructure to benchmark the next generation of scientific foundation models and intelligent agents. The toolkit is open-sourced and actively maintained to foster community-driven development and progress in AI4Science.

arXiv:2512.23508v1 Announce Type: new Abstract: How can we ensure that AI systems are aligned with human values and remain safe? We can study this problem through the frameworks of the AI assistance and the AI shutdown games. The AI assistance problem concerns designing an AI agent that helps a human to maximise their utility function(s). However, only the human knows these function(s); the AI assistant must learn them. The shutdown problem instead concerns designing AI agents that: shut down when a shutdown button is pressed; neither try to prevent nor cause the pressing of the shutdown button; and otherwise accomplish their task competently. In this paper, we show that addressing these challenges requires AI agents that can reason under uncertainty and handle both incomplete and non-Archimedean preferences.

arXiv:2512.22181v1 Announce Type: cross Abstract: Artificial intelligence (AI) is transforming cancer diagnosis and treatment. The intricate nature of this disease necessitates the collaboration of diverse stakeholders with varied expertise to ensure the effectiveness of cancer research. Despite its importance, forming effective interdisciplinary research teams remains challenging. Understanding and predicting collaboration patterns can help researchers, organizations, and policymakers optimize resources and foster impactful research. We examined co-authorship networks as a proxy for collaboration within AI-driven cancer research. Using 7,738 publications (2000-2017) from Scopus, we constructed 36 overlapping co-authorship networks representing new, persistent, and discontinued collaborations. We engineered both attribute-based and structure-based features and built four machine learning classifiers. Model interpretability was performed using Shapley Additive Explanations (SHAP). Random forest achieved the highest recall for all three types of examined collaborations. The discipline similarity score emerged as a crucial factor, positively affecting new and persistent patterns while negatively impacting discontinued collaborations. Additionally, high productivity and seniority were positively associated with discontinued links. Our findings can guide the formation of effective research teams, enhance interdisciplinary cooperation, and inform strategic policy decisions.

arXiv:2512.22242v1 Announce Type: cross Abstract: Lung cancer is the leading cause of cancer-related mortality in adults worldwide. Screening high-risk individuals with annual low-dose CT (LDCT) can support earlier detection and reduce deaths, but widespread implementation may strain the already limited radiology workforce. AI models have shown potential in estimating lung cancer risk from LDCT scans. However, high-risk populations for lung cancer are diverse, and these models' performance across demographic groups remains an open question. In this study, we drew on the considerations on confounding factors and ethically significant biases outlined in the JustEFAB framework to evaluate potential performance disparities and fairness in two deep learning risk estimation models for lung cancer screening: the Sybil lung cancer risk model and the Venkadesh21 nodule risk estimator. We also examined disparities in the PanCan2b logistic regression model recommended in the British Thoracic Society nodule management guideline. Both deep learning models were trained on data from the US-based National Lung Screening Trial (NLST), and assessed on a held-out NLST validation set. We evaluated AUROC, sensitivity, and specificity across demographic subgroups, and explored potential confounding from clinical risk factors. We observed a statistically significant AUROC difference in Sybil's performance between women (0.88, 95% CI: 0.86, 0.90) and men (0.81, 95% CI: 0.78, 0.84, p

arXiv:2512.22251v1 Announce Type: cross Abstract: Understanding how small molecules perturb gene expression is essential for uncovering drug mechanisms, predicting off-target effects, and identifying repurposing opportunities. While prior deep learning frameworks have integrated multimodal embeddings into biomedical knowledge graphs (BKGs) and further improved these representations through graph neural network message-passing paradigms, these models have been applied to tasks such as link prediction and binary drug-disease association, rather than the task of gene perturbation, which may unveil more about mechanistic transcriptomic effects. To address this gap, we construct a merged biomedical graph that integrates (i) PrimeKG++, an augmentation of PrimeKG containing semantically rich embeddings for nodes with (ii) LINCS L1000 drug and cell line nodes, initialized with multimodal embeddings from foundation models such as MolFormerXL and BioBERT. Using this heterogeneous graph, we train a graph attention network (GAT) with a downstream prediction head that learns the delta expression profile of over 978 landmark genes for a given drug-cell pair. Our results show that our framework outperforms MLP baselines for differentially expressed genes (DEG) -- which predict the delta expression given a concatenated embedding of drug features, target features, and baseline cell expression -- under the scaffold and random splits. Ablation experiments with edge shuffling and node feature randomization further demonstrate that the edges provided by biomedical KGs enhance perturbation-level prediction. More broadly, our framework provides a path toward mechanistic drug modeling: moving beyond binary drug-disease association tasks to granular transcriptional effects of therapeutic intervention.

arXiv:2512.22738v1 Announce Type: cross Abstract: Background and Objective: Biomedical Named Entity Recognition (BioNER) is a foundational task in medical informatics, crucial for downstream applications like drug discovery and clinical trial matching. However, adapting general-domain Large Language Models (LLMs) to this task is often hampered by their lack of domain-specific knowledge and the performance degradation caused by low-quality training data. To address these challenges, we introduce BioSelectTune, a highly efficient, data-centric framework for fine-tuning LLMs that prioritizes data quality over quantity. Methods and Results: BioSelectTune reformulates BioNER as a structured JSON generation task and leverages our novel Hybrid Superfiltering strategy, a weak-to-strong data curation method that uses a homologous weak model to distill a compact, high-impact training dataset. Conclusions: Through extensive experiments, we demonstrate that BioSelectTune achieves state-of-the-art (SOTA) performance across multiple BioNER benchmarks. Notably, our model, trained on only 50% of the curated positive data, not only surpasses the fully-trained baseline but also outperforms powerful domain-specialized models like BioMedBERT.

arXiv:2512.22941v1 Announce Type: cross Abstract: Heterogeneity is a fundamental property in multi-agent reinforcement learning (MARL), which is closely related not only to the functional differences of agents, but also to policy diversity and environmental interactions. However, the MARL field currently lacks a rigorous definition and deeper understanding of heterogeneity. This paper systematically discusses heterogeneity in MARL from the perspectives of definition, quantification, and utilization. First, based on an agent-level modeling of MARL, we categorize heterogeneity into five types and provide mathematical definitions. Second, we define the concept of heterogeneity distance and propose a practical quantification method. Third, we design a heterogeneity-based multi-agent dynamic parameter sharing algorithm as an example of the application of our methodology. Case studies demonstrate that our method can effectively identify and quantify various types of agent heterogeneity. Experimental results show that the proposed algorithm, compared to other parameter sharing baselines, has better interpretability and stronger adaptability. The proposed methodology will help the MARL community gain a more comprehensive and profound understanding of heterogeneity, and further promote the development of practical algorithms.

arXiv:2511.12439v2 Announce Type: replace Abstract: Online health resources and large language models (LLMs) are increasingly used as a first point of contact for medical decision-making, yet their reliability in healthcare remains limited by low accuracy, lack of transparency, and susceptibility to unverified information. We introduce a proof-of-concept conversational self-triage system that guides LLMs with 100 clinically validated flowcharts from the American Medical Association, providing a structured and auditable framework for patient decision support. The system leverages a multi-agent framework consisting of a retrieval agent, a decision agent, and a chat agent to identify the most relevant flowchart, interpret patient responses, and deliver personalized, patient-friendly recommendations, respectively. Performance was evaluated at scale using synthetic datasets of simulated conversations. The system achieved 95.29% top-3 accuracy in flowchart retrieval (N=2,000) and 99.10% accuracy in flowchart navigation across varied conversational styles and conditions (N=37,200). By combining the flexibility of free-text interaction with the rigor of standardized clinical protocols, this approach demonstrates the feasibility of transparent, accurate, and generalizable AI-assisted self-triage, with potential to support informed patient decision-making while improving healthcare resource utilization.

arXiv:2507.06092v3 Announce Type: replace-cross Abstract: Machine learning-based supervised classifiers are widely used for security tasks, and their improvement has been largely focused on algorithmic advancements. We argue that data challenges that negatively impact the performance of these classifiers have received limited attention. We address the following research question: Can developments in Generative AI (GenAI) address these data challenges and improve classifier performance? We propose augmenting training datasets with synthetic data generated using GenAI techniques to improve classifier generalization. We evaluate this approach across 7 diverse security tasks using 6 state-of-the-art GenAI methods and introduce a novel GenAI scheme called Nimai that enables highly controlled data synthesis. We find that GenAI techniques can significantly improve the performance of security classifiers, achieving improvements of up to 32.6% even in severely data-constrained settings (only ~180 training samples). Furthermore, we demonstrate that GenAI can facilitate rapid adaptation to concept drift post-deployment, requiring minimal labeling in the adjustment process. Despite successes, our study finds that some GenAI schemes struggle to initialize (train and produce data) on certain security tasks. We also identify characteristics of specific tasks, such as noisy labels, overlapping class distributions, and sparse feature vectors, which hinder performance boost using GenAI. We believe that our study will drive the development of future GenAI tools designed for security tasks.

Background: Background: Digital health technologies have the potential to revolutionize the screening, diagnostic support, monitoring and intervention of early cognitive change. However, the full spectrum of their application and the existing evidence base in this specific patient population have not been systematically delineated. Objective: Objective: To review and synthesize digital health technologies' applications, roles, and challenges in patients with early cognitive changes. Methods: Methods: This scoping review followed the enhanced Arksey & O'Malley Framework and PRISMA-ScR guidelines. A comprehensive search of four databases (PubMed, Embase, Web of Science, and Cochrane Library) was conducted from their inception until May 31, 2024. Studies were selected and data were extracted using the Population-Concept-Context framework, focusing on digital health interventions for patients with early cognitive changes. Results: Results: A total of 163 articles were included in this review, revealing a notable increase in the use of digital health technologies for patients with early cognitive changes since 2020. Of the studies, 162 focused on Mild Cognitive Impairment(95.1%), 10 on Subjective Cognitive Decline (6.1%), and 7 examined caregiver support (4.3%). The technologies were categorized into six groups: Smartphone/Computer Application, Virtual Reality, Artificial Intelligence/Big Data, Robotics, the Internet of Things, and Telemedicine. The clinical outcomes demonstrated statistically significant improvements in cognitive performance, and patient-reported outcomes including overall well-being, quality of life and social engagement. But digital health technologies also present implementation challenges, such as Virtual Reality induced vestibular symptoms, connectivity issues in telemedicine, and unintended negative consequences. Conclusions: Conclusion: This review affirms the efficacy of digital health technologies (DHTs) in screening, diagnosing, intervening in, and monitoring early cognitive changes. However, challenges such as cost, technical complexity, and user engagement currently impede broader adoption. In conclusion, while DHTs demonstrably enhance healthcare professional efficiency in managing early cognitive impairment—by facilitating clinical decision-making, optimizing patient management, and enabling personalized care—overcoming existing implementation barriers remains critical. Furthermore, rigorous assessment of their long-term effects through future research is essential. Collectively, these findings underscore the substantial potential of DHTs to transform cognitive health management and patient care, offering valuable insights for healthcare professionals, researchers, and policymakers to optimize these solutions. Clinical Trial: Trial Registration: No Trial Registration.

Cell Death Discovery, Published online: 29 December 2025; doi:10.1038/s41420-025-02917-8

Modeling hepatocellular carcinoma and its microenvironment on a chip

npj Digital Medicine, Published online: 30 December 2025; doi:10.1038/s41746-025-02252-3

Computational network models for forecasting and control of mental health trajectories in digital applications

Nature Medicine, Published online: 29 December 2025; doi:10.1038/s41591-025-04027-5

An analysis of 204 countries estimates that diabetes will cost the global economy $10.2 trillion between the years 2020 and 2050.

Many people use peer online forums to seek support for health-related problems. More research is needed to understand the impacts of forum use, and how these are generated. However, there are significant ethical and practical challenges with the methods available to do the required research. We examine the key challenges associated with conducting each of the most commonly used online data collection methods: surveys, interviews, forum post analysis; and triangulation of these methods. Based on our learning from the Improving Peer Online Forums (iPOF) study, an inter-disciplinary realist informed mixed methods evaluation of peer online forums, we outline strategies that can be used to address key issues pertaining to assessing important outcomes, facilitating participation, validating participants (users who consent to take part in one or more parts of the study), protecting anonymity, gaining consent, managing risk, multi-stakeholder engagement, and triangulation. We share this learning to support researchers, reviewers, and ethics committees faced with deciding how best to address these challenges. We highlight the need for ongoing open, transparent discussion to ensure the research field keeps pace with evolving technology design and societal attitudes to online data use.